Job-shop scheduling optimization tools attempt to schedule various jobs for processing on various available resources in the most optimal way possible. However, in order to remain computationally tractable, job-shop scheduling optimization problems take into consideration only a sub-set of all resources needed to run operations in a manufacturing facility. For example, all tools or stations in a shop floor may be taken into account, but all material handling systems might be ignored. While one could design the material handling system, e.g., with cranes, transfer cars, guided automated vehicles etc., to have a capacity much higher than the overall throughput capacity of the stations in the floor, in reality physical space availability and complexity of movement constraints often limit the capacity of the material handling system to be equal to or slightly above the capacity of the rest of the system.
Resources are ignored in the formulation of a scheduler either because their effect on the scheduling is perceived to be minimal or because they are more complex to model compared to a fixed-location machine. For example, if the jobs are moved from a machine to the next resource using cranes on the same rail, then the location and status of each crane has to be tracked in order to serve a move request. This increases the scheduling problem size to intractable levels.
Another simplifying assumption made in job-shop scheduling is to assign deterministic values for many parameters that actually are considered to have random values. While many machine operations can be controlled to the point where they are essentially deterministic some values like “good yield of a chemical process”, “time taken to move between machine A and B” etc. are much harder to predict due to the complexity of manufacturing units and material handling systems. Thus those values are better treated as random variables. However, most scheduling optimization formulations do not handle them as random parameters because of computational time constraints.
As a result of the limitations discussed above schedules generated from job-shop scheduling formulations might not be viable in terms of their applicability to the actual manufacturing facility. For instance, the assumed travel time between stations might not always be feasible, which may lead to disruptions in the order of processing the jobs as prescribed in the generated schedule. Since job-shop schedulers are typically used when the sequence of operations and their particular timing constraints are crucial to production, this can lead to significant wastage and increased cost of operations.
To illustrate these problems, consider the operation of a steel plant. Different sub-divisions of a steel plant are usually operated with the help of sophisticated optimization-based schedulers. Crucial manufacturing constraints like sequencing of similar grades of steel lots together to avoid costly set-up times, sequencing the next operation within a preset time-window after completion of current operation in order to avoid cool-down of molten metal etc. are best handled by a scheduler using an optimization-based formulation of these constraints and objectives. However, important resources like the cranes and transfer cars needed to move large ladles filled with molten metal, and the ladles themselves are not included in the formulation. Often, the stochasticity inherent in the arrival times of molten metal from the blast furnace, the time taken to get a free crane or transfer car, and the time to move between machines etc. are ignored and these parameters assumed deterministic. If these variables are assigned to poor deterministic values that are often violated in the real world, the resultant schedule might often break when applied. This will lead to lots not arriving at locations close to their assigned times, resulting in breaks in long sequences, increased machine idle time, and the need for re-processing to heat up cold metal.
Michael C. Fu, 2002, Optimization for Simulation: Theory vs. Practice, INFORMS Journal on Computing Vol. 14, No. 3, pp. 192-215; Andradóttir, S. 1998. Simulation optimization. Chapter 9 in J. Banks, ed. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice. John Wiley & Sons, New York; Andradóttir, S. 2006. An overview of Simulation optimization via random search. Chapter 20 in S. G. Henderson and B. L. Nelson, eds. Simulation: Handbook in Operations Research and Management Science Vol 13. Elsevier, Amsterdam, survey using simulations to evaluate the candidate solutions produced by an optimization formulation. The methods described there, however, use the simulation to simply evaluate how good a candidate solution is. They specifically do not use the information provided to change the optimization formulation itself; the information is instead used simply to guide the search for a better candidate using the same formulation.
J. Atlason, M. A. Epelman and S. G. Henderson. 2002. Call center staffing with simulation and cutting plane methods. Annals of Operations Research 127, 333-358, describes a procedure to use simulation to change certain parameters in an optimization formulation. In that procedure, however, the structure of the formulation remains static, only the coefficients or parameters of the model are changed given new information. That procedure also does not broaden the formulation by modeling previously left-out resources of the system, for example, based on simulation results.